Cognee vs PLCGateway AI MCP

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Cognee compared with PLCGateway AI MCP
Detail	Cognee	PLCGateway AI MCP
What it does	Open-source memory platform for AI agents	MCP server for industrial software that lets AI assistants connect machines, build screens and analyze production data
Stage	Not given	Not given
Founded	Not given	Not given
Based in	DE	TR
Pricing model	Not given	Not given
Pricing	Free: $0/month with 1M tokens and 1 workspace (no card required). Standard: $1.00 per 1M tokens plus $5 per additional workspace. Enterprise: contact for pricing with bring-your-own-cloud deployment. Open-source engine available free to self-host.	Not given
Key features	Unified vector search, knowledge graphs, and relational storage Automatic knowledge graph generation from data sources Multi-tenant support with isolated workspaces Data source integrations (Slack, Notion, Google Drive, GitHub, etc.) Self-improving memory that learns from use Multiple deployment options (cloud, self-hosted, air-gapped) Agent framework integrations (LangGraph, CrewAI, OpenAI SDK, MCP) Support for multiple vector and graph databases GDPR compliance and encryption at rest and in transit Built-in graph visualization of company brain	Connect machines and create devices from plain language instructions Build process screens and report dashboards that engineers review before deployment Define production assets and calculate OEE for discrete, continuous or batch processes Query alarm statistics and production data in natural language Support MCP protocol over network HTTP or local stdio connections Works with any MCP-compatible AI client including desktop and self-hosted models Role-based access control matching viewer, operator and administrator permissions Audit logging of every write operation with user identification Acknowledge alarms and record batch results within user permissions Generate Python code examples for REST API integration
What makes it different	Combines vectors with graph relationships and generated data models for connected context retrieval State of the art performance on BEAM benchmark (79% vs 73.4% at 100k tokens) Keeps memory costs flat as data grows compared to baseline LLM approaches Fully open-source with no database lock-in Updates memory as source data changes, not just static snapshots	Built-in MCP server in every product, no separate AI add-on or cloud service needed Production data remains on-premise; nothing sent to cloud unless user's chosen AI model requires it Drafts protect production: AI-created screens and dashboards reviewed before going live Same access controls for AI as for people: roles, rights and logging Assists in user's own language and explains PLCGateway features through built-in help
Free plan	Not given	Not given
Open source	Not given	Not given
Platforms	Not given	Not given
Public API	Not given	Not given
Community votes	0	0
DR (Domain Rating by Ahrefs)	70 no change since last check	Not checked yet no change since last check
Trust Flow (Majestic)	14	0
Citation Flow (Majestic)	43	0
Referring domains (Majestic)	1089	0

4 details filled in for both companies.

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